class_docstr: Create an operator that returns a tensor using a Laplacian filter.
  
  The operator smooths the given tensor with a laplacian kernel by convolving
  it to each channel. It supports batched operation.
  
  Args:
      kernel_size: the size of the kernel.
      border_type: the padding mode to be applied before convolving.
        The expected modes are: ``'constant'``, ``'reflect'``,
        ``'replicate'`` or ``'circular'``.
      normalized: if True, L1 norm of the kernel is set to 1.
  
  Shape:
      - Input: :math:`(B, C, H, W)`
      - Output: :math:`(B, C, H, W)`
  
  Examples:
      >>> input = torch.rand(2, 4, 5, 5)
      >>> laplace = Laplacian(5)
      >>> output = laplace(input)
      >>> output.shape
      torch.Size([2, 4, 5, 5])
code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}"
          f"(kernel_size={self.kernel_size}, "
          f"normalized={self.normalized}, "
          f"border_type={self.border_type})"
      )
  
file_path: kornia/kornia/filters/laplacian.py
imports: from __future__ import annotations
  from kornia.core import Module, Tensor
  from .filter import filter2d
  from .kernels import get_laplacian_kernel2d, normalize_kernel2d
label_desc: Use image node_id 2 for calling the Laplacian obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str, self, str